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nasza = read.csv("nasza.csv", header=T, stringsAsFactors = F) | |
library("openxlsx") | |
library(dplyr) | |
miRtarbase = openxlsx::read.xlsx("hsa_MTI.xlsx") | |
refdb = filter(miRtarbase, Support.Type == "Functional MTI" | Support.Type == "Non-Functional MTI") | |
miR = unique(nasza$hsa.miRNA) |
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library(plyr) | |
library(dplyr) | |
library(edgeR) | |
library(epiDisplay) | |
library(rsq) | |
library(MASS) | |
library(Biocomb) | |
library(caret) | |
library(dplyr) | |
library(epiDisplay) |
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suppressMessages(library(rmdformats)) | |
suppressMessages(library(dplyr)) | |
suppressMessages(library(plyr)) | |
suppressMessages(library(tidyverse)) | |
suppressMessages(library(reshape2)) | |
suppressMessages(library(xlsx)) | |
suppressMessages(library(psych)) | |
suppressMessages(library(knitr)) | |
suppressMessages(library(gplots)) | |
suppressMessages(library(car)) |
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library(OmicSelector); set.seed(1); | |
if(!dir.exists("/OmicSelector/temp")) { dir.create("/OmicSelector/temp") } | |
OmicSelector_load_extension("deeplearning") | |
library(data.table) | |
#use_session_with_seed(1) | |
#selected_miRNAs = c("hsa.miR.192.5p", | |
# "hsa.let.7g.5p", | |
# "hsa.let.7a.5p", | |
# "hsa.let.7d.5p", | |
# "hsa.miR.194.5p", |
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library(readxl) | |
library(dplyr) | |
dane <- read_excel("dataset_uzupelniony.xlsx") %>% mutate_if(is.character,funs(factor(.))) |
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library(ggpubr) | |
library(ggsci) | |
temp = data.frame("Przyjęcie" = dane$Poczatkowe.stezenie.kreatyniny.w.trakcie.hospitalizacji., "Wypis" = dane$Stezenie.kreatyniny.przy.wypisie.) | |
a = ggpaired(temp, cond1 = "Przyjęcie", cond2 = "Wypis", palette = "npg", xlab = "", ylab = "Kreatynina", line.color = "grey", point.size = 0.01) | |
describe(temp) | |
wilcox.test(temp$Przyjęcie, temp$Wypis, paired = T) | |
t.test(temp$Przyjęcie, temp$Wypis, paired = T) | |
gridExtra::grid.arrange(a, ncol = 1) |
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# -*- coding: utf-8 -*- | |
options(reticulate.conda_binary = "/opt/conda/bin/conda") # conda path | |
library(survival) | |
library(ranger) | |
library(ggplot2) | |
library(dplyr) | |
library(readr) | |
completed = read_csv("pirads_v0_complete.csv") |
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#!/bin/bash | |
wget https://github.com/sebanc/brunch/releases/download/r97-stable-20220121/brunch_r97_stable_20220121.tar.gz | |
# Chrome OS image: https://cros-updates-serving.appspot.com/ | |
wget https://dl.google.com/dl/edgedl/chromeos/recovery/chromeos_14324.62.0_rammus_recovery_stable-channel_mp-v2.bin.zip | |
sudo apt install -y pv cgpt | |
tar xvzf brunch_*.tar.gz | |
unzip chromeos_*.bin.zip | |
sudo bash chromeos-install.sh -src chromeos_*.bin -dst chromeos.img -s 32 # 32 GB image |
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mklink /J "C:\Projekty\" "C:\Users\Konrad\OneDrive - Uniwersytet Medyczny w Łodzi\" |
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# This code is a part of paper entitled: "Deep learning-based integration of circulating and cellular miRNAs expression of pancreatic cancer" | |
# Author: Konrad Stawiski, MD, PhD (konrad@konsta.com.pl; https://konsta.com.pl) | |
# Based on https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-12-8 | |
library(dplyr) | |
setwd("C:/Users/konra/OneDrive/Doktorat/Analiza/Szacowanie") | |
set.seed(1) | |
# Get exemplary file | |
Elias2017 <- data.table::fread("Elias2017.csv") |
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